Figure 1 is an overview of our merged dataset. It contains CO₂ emissions, forest percentage cover, GDP per capita, and energy use per capita annual time series data from 1990 to 2020. We are using linked/interactive choropleth map to help users discover the dataset. From the choropleth map, we can see there are three leading countries, China, the United States, and Russia, whose CO₂ emissions took ~50% of the World's total emissions in 2020. Each country has unique trends of other factors. For example, China has an increasing trend of forest coverage. This led to our further analysis of the details of each factor. Another interesting thing we found from this figure is that the CO₂ emissions stacked bar plot by country show that U.S. and China switched their roles around 2006. Before 2006, the U.S. had higher emissions, while after 2006, China took place.

This view is generated using Altair package in python. The color is in log scale so that the large range of the CO₂ emissions can be presented well on the map. The color scheme is chosen based on our green theme. This figure can also be considered an innovative view due to it being a two-level linked plot. By clicking the countries, the bar plots are filtered to the specific country. The users can also choose the year of interest in any bar plot. Consequently, the same year of other plots would be highlighted accordingly.

Figure 1 shows positive relationship between GDP and CO₂ emissions. In addition, Figure 2 shows country income type is also affect the CO₂ emissions distributed by sector. Here, we conduct Figure 4 to see the detailed change of CO₂ emissions with GDP.

Figure 4 is a linked scatter plot with a horizontal bar plot at the bottom developed by Plotly package in python. The plot contains a slider with animation. Data from 1990 to 2019 will be displayed after the click. The scatter plot takes the emission of CO₂ in a country on the x-axis, the emission of CO₂ per capita in a country on the y-axis. The bubble size represents the population of a specific country, and the color of the bubbles represents the income group corresponding to a country. In the bar plot, the x-axis represents the overall CO₂ emission number, and the y-axis represents different income groups. The plot is interactive, and hovering over the bubbles displays detailed information about each data point, such as country, total emission, emission per capita, income group, and population. And hovering over the bar chart displays detailed information about the specific income group and the overall emission of this group.

The scatter plot was inspired by the “gapminder” plot but with improvements by adding a horizontal bar chart to carry more information. The color encodes to correspond with income groups. Bright red was used to represent a high income group, while light green is used to represent a lower-income group, which matches Figure 2. The initial development of the plot used bokeh package, but due to the requirement of a server for the interactive bokeh plots, it was ultimately switched to the plotly package.

Before the year of 2010, high income countries have always been the main contributors to CO₂ emissions. As shown in the plot, the small-sized bubbles are always located above others, which indicates that the high-income countries have smaller populations but higher emissions per capita. Lower-middle income countries, with their large population size, have consistently ranked second in total CO₂ emissions, despite their low per capita emission rate. However, this trend changed after 2010, with upper-middle income countries surpassing high-income countries to become the largest contributor to global CO₂ emissions in terms of total emissions. The reason for this shift is not hard to find: some countries changed their income category and brought their high emissions to the new category as well. This is a crucial point to be considered, as without paying attention to the emissions from large population countries, CO2 emission will be uncontrollable with the period of economic development.

Figure 1 shows positive relationship between energy use and CO₂ emissions. Here, we conduct Figure 5 to exam the detailed distribution of CO₂ emissions by fuel type for five sample countries adopted different forest strategy (Figure 3). When comparing the CO₂ emissions from different energy sources for each country, we can observe the following: For coal, China has the highest reliance on coal (75%), followed by the United States (41.6%), Russia (38.8%), Canada (28.5%), and Brazil (14.9%). This indicates that China's CO₂ emissions are heavily influenced by coal consumption, which has significant environmental implications. For oil, Brazil has the highest reliance on oil (68.7%), followed by Canada (44.1%), the United States (38.2%), Russia (28.3%), and China (13.8%). Brazil's transportation and industrial sectors contribute significantly to its CO₂ emissions due to their dependence on oil. Russia has the highest reliance on natural gas (29.8%), followed by Canada (24.3%), the United States (18.6%), Brazil (7.97%), and China (2.87%). While natural gas is a cleaner-burning fossil fuel, it still contributes to CO2 emissions, with Russia being the most dependent on gas among these countries.

In summary, China has the highest dependence on coal, Brazil has the highest dependence on oil, and Russia has the highest dependence on natural gas among the five countries. Brazil also has the highest percentage of CO₂ emissions from non-fossil fuel energy sources and some industrial processes. These differences in energy source reliance reflect each country's unique energy mix and the historical development of its energy infrastructure.

This plot is an innovative view. The plot is generated using R Plotly package. The color matches the energy use in Figure 1. We have ordered the fuel types same across the countries. We used clip charts in the middle of each pie chart to indicate the major energy source of each country.

After conducting our analysis, we have come to the conclusion that a direct, simple relationship between emissions and forest cover cannot be visually confirmed. While increasing forest coverage theoretically helps to absorb CO₂, there are other major factors at play that impact CO₂ emissions, making the effect of forest coverage changes negligible. One such factor is the high usage of fuel, particularly coal, oil, and gas, which are the dominant fuel types burned by the sample countries. Interestingly, this factor does not appear to have any visible impact on the overall trend of global emissions during the selected time period.
However, when examining individual countries, such as China, it is clear that energy usage has shown a steady increase from 1990 to 2015, while others, such as the U.S., have remained at the same level. The high usage of fuel may also be linked to income levels, as upper-middle and high-income countries, whose major emissions are contributed by Electricity and Heat, have been responsible for most of the global emissions in the past three decades. In fact, higher total emissions have been positively related to emissions per capita. In summary, while forest coverage is important in the fight against climate change, it appears that other factors, particularly energy usage and income levels, have a more significant impact on global emissions trends.